Development and implementation of an intelligent early warning system for preventing environmental pollution from coal spontaneous combustion

作者：Biao Kong, Huijin Wan, Sixiang Zhu, Wenrui Zhang, Shuanglin Song, Xiaolong Zhang, Xueping Sun, Wei Wang, Dong Ma, Zhenlu Shao, Laifeng Jiang, Caihua Shi · 发表于：Green and Smart Mining Engineering · 年份：2025 · DOI：10.1016/j.gsme.2025.09.005 · 被引用次数：34 · 研究领域：Coal Properties and Utilization、Environmental and Industrial Safety、Safety and Risk Management

Coal spontaneous combustion fires threaten personal safety, increase carbon emissions, release toxic and harmful gases, and cause serious environmental pollution. The study of intelligent early warnings for coal spontaneous combustion can advance fire prevention and control measures, making a meaningful contribution to the ecological and environmental protection in mining areas. To address the limitations in selecting characteristic index gases for coal spontaneous combustion and the low accuracy of traditional temperature prediction and discrimination models, an intelligent identification system was developed. The system integrates laboratory research and analysis, intelligent algorithm optimization, index rationality verification, and field measurement and application, all based on characteristic index gases. By constructing a dynamic discriminant model of coal self-gas temperature, the composite index of coal spontaneous combustion characteristics is further optimized and verified. Model performance was evaluated using root mean square error (RMSE), decision coefficient ( R 2 ), mean absolute error (MAE), and mean absolute percentage error (MAPE). The prediction results for three, four, and five parameters were obtained. The results indicate that the R 2 value was 0.9975 under the conditions of O 2 , CO, C 2 H 4 , and CH 4 /C 2 H 6 , demonstrating the best model performance. The MAE was 1.9272, the RMSE was 2.5114, and the MAPE was 2.0830%. These findings enable optimal se...